Measuring Food Insecurity in Guatemala

A Senior Honors Thesis

Presented in Partial Fulfillment of the Requirements for graduation with distinction in Human Nutrition, Dietetics track, in Human Ecology at The Ohio State University

By Tessa Acker

The Ohio State University June 2011

Project Adviser: Hugo Melgar-Quninonez, MD, Professor, Department of Human Nutrition

This thesis is an internal and criterion validation study on the Latin America and

Caribbean Food Security scale (Escala Latinoamericana y Caribena de Seguridad

Alimentaria - ELCSA) conducted in over 200,000 households in Guatemala. A fifteen-

question survey was distributed in eight of the thirteen departments of Guatemala; from

the indigenous western region, to the forested, impoverished northwest region. The

eastern region was excluded. This study demonstrated the efficacy of the survey in

measuring the severity of household food insecurity. The data were received from the

Guatemala National Institute of Statistics and analyzed using the Rasch model to

determine the survey’s internal validity, through use of a severity scale and infit values.

The criterion validity was supported through use of 1-way ANOVA and chi square

statistics and demonstrated statistically-significantly correlations between the food

insecurity status found in this study and other previously identified food insecurity

factors. This study further documented the Latin American and Caribbean Food Security

scale as an internally and externally valid instrument recommended for use in national

representative surveys to measure household food insecurity. A valid and reliable tool to

measure food insecurity is necessary to successfully target at-risk and high-risk

populations and to efficiently implement and monitor interventions.

Introduction

Food insecurity is defined as, “not having adequate physical, economical, or

social access to nutritious food that meets dietary needs for an active and healthy life1.”

In 1974 at the World Food Conference, for the first time, adequate food supply was

examined on an individual basis, rather than a country basis. At the national level, many

countries seemed to have adequate food supply, deeming inhabitants “food secure.”

However, when these countries were examined more closely on an individual basis,

widespread and intense hunger was discovered among millions of people worldwide, in

addition to the “hidden hunger” experienced by one-third of the world’s vitamin and

mineral deficient inhabitants2. Hunger is measured by an energy intake below that which

is required to maintain body weight, body composition, and levels of physical activity for

long-term good health2. In an effort to reduce the number of suffering citizens, the 1996

Rome World Food Security re-evaluated the meaning of food security and revisited the

Universal Declaration of Human Rights, which proclaimed meeting nutritional needs as a

right, rather than a privilege, for all (Article 25 Adopted and proclaimed by the General

Assembly of the united Nations resolution 217 A (III) of 10 December 1948). The

committee set the goal to reduce by 50 percent the number of hungry people by 2015

when benchmarked by the 1990 level (World Food Summit Plan, Millennium

Development Goal- MDG)1. The World Food Summit Plan of Action vowed to

“implement policies aimed at eradicating poverty and inequality and improving physical

and economic access by all, at all times, to sufficient, nutritionally adequate and safe food

and its effective utilization1.” Regrettably, in 2010 the number of hungry people has

instead more than doubled, and there are over 945 million hungry people around the

world, as opposed to the projected 412 million come 20151.

Food insecurity occurs for a variety of reasons and has multiple dimensions.

Geographic and socioeconomic factors contribute to food security because they can limit

access and supply of food3. USAID’s Mesoamerican Food Security Early Warning

System reports that 15,000 Guatemalans will be food insecure in 2011 as a result of

climatic events4. Another 486,000 individuals will need food assistance due to loss of

crops and livelihoods in the eastern, steeper regions of Guatemala4. Other crises include

natural disasters, human disasters, or a combination of both3. From 2006-2009 there was

a sharp increase of hunger throughout the world due to high food prices and the global

economic crisis1.

Improper or inadequate nutrition from a young age can result in protein and

energy deficiency (as found in one-quarter of the world’s children) that results in

stunting, wasting, and underweight children5. Malnutrition can weaken the immune

system, increasing the risk for infection, and can delay the growth and cognitive

development of children2. Micronutrient deficiencies can lead to large range of diseases

and other health deformities. Vitamin A, iron, and iodine are micronutrients with the

most harmful consequences2. The lack of essential vitamins and minerals are often not

visible to the eye, contributing to the name “hidden hunger” used to describe this critical

issue2. After targeting food insecure households, further analysis of these deficiencies can

be explored, helping accomplish the MGDs’ goal of reducing infant and maternal

mortality, and the prevalence of HIV-AIDS, malaria, and other life threatening diseases1.

Physiologic and social stresses, including anxiety, volatility, sadness, and depression, are

often additional consequences of not having enough food6. Aside from the severe health

issues that can result from food insecurity, the disparity felt by individuals can cause

conflict and political unsteadiness6.

The world population and food system also can contribute to food insecurity. In a

study predicting the future of the global food system, it is predicted that the world

population will reach nine billion by 20509. With this increased demand in food, it is

critical to determine how to feed this rising population, while still trying to assist the

current population. Policy makers must be aware of the “nutrition transition” occurring

around the world, especially in developing countries. This phenomenon pertains to the

replacement of native foods with an increased consumption of imported highly processed,

high-fat, or high-sugar foods10. The fast food and large supermarket environment that is

prevalent in the United States today, developed over a period of 50 years. What took a

developed nation half a decade to occur, has taken only 10 years in Latin America9.

Guatemala continues to have the highest rate of chronic malnutrition in the

western hemisphere, reporting 49% of the nation being malnourished4. Child chronic

malnutrition is even more devastating, plaguing 69% of the indigenous Guatemalan

children. Since little progress has been made on these statistics since 1995, at this rate, it

would take 83 years to eliminate stunting within the indigenous population4.

Measuring household food insecurity and its consequences is necessary because it

provides an estimate of the prevalence and causes of hunger so that policy makers can

better target and intervene to aid high-need populations7. Supported by accurate data,

proper evaluation systems can be implemented to improve the security of food and

alleviate the consequences of food insecurity. Food insecurity should measure not only

reductions in food quantity, but also food quality, for studies have shown fruits and

vegetables are among the first food groups to be eliminated when money is short8.

Previously, food insecurity was measured on national and regional levels based on

economic indicators of food production and food availability11. The most common

methods to measure food insecurity include: national levels of dietary energy supply,

individual food intake reports, anthropometric measures, and questionnaires measuring

experiences of food security12. The first three approaches can be timely, expensive, and

lack the ability to measure the “experience” of the household. The questionnaire

approach excels in exploring the psychometric and physical conditions of each household

individually, is less expensive, easier to use, and can be applied to a diverse amount of

populations12.

The Latin American and Caribbean Food Security Scale

In an effort to quantify hunger in the United States, the National Nutrition

Monitoring and Related Research Act was passed to standardize tools to measure and

obtain data on food insecurity around the nation13. In 1992, the US Household Food

Security Supplemental Module (HFSSM) was created from a hunger index defined by the

Massachusetts Nutrition Survey (1983) and further investigations13. Using this module as

a framework, similar surveys have been distributed on five different continents to

measure food insecurity and explore the factors with which it is associated. This HFSSM

survey has been translated and modified to fit different cultures, such as Latin America

and the Caribbean14.

A food security status was generated in this study based on the respondent’s

number of affirmative answers to the distributed survey. Food insecurity is classified into

four categories: food secure, mildly food insecure, moderately food insecure, and

severely food insecure15. The fifteen-question survey tests both psychometric underlying

conditions of the households, as well as the physical food-related conditions. The survey

uses questions that progressively increase in severity. The insecurity score is designed to

increase based on affirmative responses to questions that indicate a higher level of food

insecurity, and should encompass all of the previous, less severe responses. The first

eight survey questions inquire about the food-related condition of the household, while

the last 7 questions ask about the child’s experience in the household. The survey is

designed this way with the assumption that children are “protected” within the household,

meaning they are the last to feel the food insecurity15. In theory, the quality of the food

will first decrease, then the quantity of the adults’ food, then the quantity of the children’s

food, which if answered affirmatively, would indicate a severely food insecure

household. It is important to note that the survey as a whole is used to indicate the level

of food insecurity, not each question separately.

If a tool is valid, it will measure what it is intended to measure, in this case, the

level of food insecurity17. Typically, to determine validity, a tool is typically compared to

a “gold standard.” Though there is not yet a gold standard for measuring food insecurity,

there are factors that have been demonstrated to be linked to this condition including:

poverty, access to public services, structural material of house, number of occupants per

room per house, and agricultural production of the country. This study tested internal

validity, meaning it measured how valid an instrument is in measuring within itself or

within a population17. The instrument is deemed reliable if it provides a consistent and

reproducible measure every time it is used. This study will further evaluate the reliability

of the Rasch model in regards to measuring food insecurity.

Criterion validity, on the other hand, demonstrates the accuracy of a tool by

comparing it to an existing valid measurement or tool17. The food security status

determined in this study was run against other “criterion” that might be linked to food

insecurity. A strong correlation between the two measures indicates criterion validity17.

Methods

In all, 265,229 Guatemalan households completed the Latin American and

Caribbean Food Security Scale survey. Inhabitants within eight of the twenty-two

departments in Guatemala responded to this survey. The east side of Guatemala was not

included, for this area of the country is least indigenous. Samples were received from

both urban and rural areas; the instrument was expected to perform the same in each

setting.

A total of 295,243 households responded to this survey. Surveys were completed through

interviews. The interviewers were standardized under the same methodologies, though

the condition of the interview was not standardized. The interviews were done in May,

June, and July 2010, in the second half of each month. Harvest season in Guatemala is

after July. With these conditions, food insecurity was expected to be higher than if the

interview were conducted at the beginning of the month or after the harvest season.

Households that responded only to the last seven questions regarding the child’s

experience and that failed to respond to the first eight regarding the household were

omitted (n=30,014). These households therefore reported incomplete information and

were left out under the terms that, comparatively, so few households committed this

mistake. All departments within Guatemala were affected by dropping these participants,

particularly department six, which was deleted entirely. The final number of respondents

was counted under the conditions that all of the first eight questions about the household

were responded to and if the household contained children, all seven final questions were

completed.

Using STATA statistical software the data were revised for further analysis using

Winsteps modules 3.69.1.618. In order to properly analyze this data with regards to

increasing severity, the data needed to be transformed. In the beginning, an affirmative

answer counted as one, while a negative answer counted as two. Because the survey

generally increased with severity with each question, more affirmative answers should

equate to a higher number, thus a higher severity. To correct this scale, the answers were

recoded, “yes” equaling one, “no” equaling zero. Based on the number of affirmative

answers, the household was then classified into a “food security status,” used in the

criterion validity portion of the study (Table 1).

The Rasch model

In general, Rasch analysis models item difficulty/severity as a log transformation

of the probability of a person responding to a given item in a certain way. The Rasch

model compares dichotomous data, transformed from the human sciences, against a

mathematical framework and assesses the fitness and internal validity of the tool used to

measure the data20. In an academic setting, the Rasch model can be used to measure

cognitive ability of a student; it assumes as the difficulty of the question increases, the

likelihood of a student responding correctly decreases. The number of correct answers

before an incorrect answer should be consistent with the scale, increasing in a steady,

straight line, assuming the student correctly answered the previous “easier” questions.

The same model and analysis can be used when measuring household food insecurity

status. It is assumed that if a participant answers yes to a question that dictates severe

food insecurity (such as “child goes to bed hungry”) all other less severe food condition

questions should also be answered affirmatively. The Rasch model has been used to

determine the internal validity of other household food security surveys in the past19,20.

The Rasch model generates mean square fit statistics, which measure the

difference between the expected and the actual responses20. One type, information

weighted, or “infit” values are commonly used in food insecurity studies, to determine

the appropriateness of the model based on the responses of the participants21. As a general

rule, infit values of 0.8-1.2 is recommended, 0.7-1.3 values are considered good, and 0.6-

1.4 are considered adequate. A score of 1 indicates perfect coherence with the model

predictions; an infit value higher than one shows a fit to the model with more variation

than expected; values below one signify a better than expected fit or less variation that

the model predicts in the observed response pattern21. The Joint Maximum Livelihood

Estimation (JMLE) explains why the first question is often overestimated (having an infit

value greater than 1), because the first question has no previous question for comparison.

This survey is considered a short tool, with less than 25 questions, and displays some

evidence of bias (question one infit value= 1.39). This bias results from the possibility of

extreme scores within the estimation space, without the ability to measure them 22.

However, the sample size is large enough to significantly reduce the impact of the bias.

A test item is considered biased when the item within this survey is found to misrepresent

what is being measured, putting one group at a disadvantage in taking the examination23.

The severity values of the fifteen items in the survey were also computed using

the mean-square fit statistics of the Rasch model. Each survey item was run in order of

relative severity, and assigned a relative severity value. Differential Item Functioning

(DIF) was used to evaluate whether there were differences between urban and rural

populations. DIF is displayed when there is a significant difference in the probability of

respondents from two distinct groups (such as male vs. female or urban vs. rural) 23. DIF

CONTRAST is estimated by subtracting each DIF item calibration by area. A DIF

CONTRAST greater than or equal to .5 logit units is substantial, demonstrating a bias

within the data23.

Criterion validity

A 1-way ANOVA test and a 4x2 chi square significance test were run to generate

a Food Security Status (4 categories) based on the number of affirmative answers to the

survey. The classifications were grouped based on the guidelines produced by ELCSA

(Table 1).

Households With Children Food Security Status Classification

0 Food secure

1-5 Mildly Food Insecure

6-10 Moderately Food Insecure

11+ Severely Food Insecure

Households Without Children Food Security Status Classification

0 Food secure

1-3 Mildly Food Insecure

4-6 Moderately Food Insecure

7-8 Severely Food Insecure

Table 1 The Guidelines to determine a household (with or without) food security status, developed by the ELCSA committee15.

The distributed survey not only collected responses to the fifteen questions, but

also reported the conditions of the household including: construction material of the

house, the level of crowding, the number on appliances and vehicles, poverty level,

number of rooms/bedrooms, and access to public services (water, sewer, electricity, and

phone). This information was collected at the same time the food security scale was

presented.

The mean values of each category were formulated into a principal component, by

calculating the mean value within each factor. Then, a 1-way ANOVA test was run

between each mean value (continuous variable) with the assigned food security status of

each household. This test was adjusted using the Bonferroni multiple comparison test.

The test was run to determine differences among sub factors within each category. For

example: within the household construction materials, a Bonferroni multiple comparison

test was run between each household material to test for a difference between each

individual factor. If a significant difference is found between each material, one can

conclude that each material contributes to the relation to the food security status, not just

the general factor of “household material.”

Results

First, a descriptive analysis was created for the study. The survey was completed

in thirteen of the twenty-two Guatemalan departments.

Area n(%)

Urban 75,255 (.28)

Rural 189,971 (.72)

Table 2 The distribution of areas completing the ELCSA survey in Guatemala.

Household Characteristics Mean number

People per house 5 ± 2.4

Number of Rooms 2.0 ±1.2

Number of Rooms that are Bedrooms 1.6±0.9

Table 3 Household characteristics of the households surveyed in Guatemala.

Poverty Level n(%)

Not Poor 117,686 (.44)

Poor 84,288 (.32)

Critically Poor 63,255 (.24)

Table 4 Number of households in the Guatemalan sample survey in each poverty status, as classified by the

Guatemala National Institute of Statistics.

Households with Kids n(%)

Yes 217,334 (.82)

No 47,895 (.18)

Table 5 The number of households in the sample population containing kids. Having kids increases the risk and severity of food insecurity.

The physical conditions of the house were reported as well:

Exterior Wall Material n(%)

Block 154,642 (.58)

Wood 39,852 (.15)

Adobe 26,240 (.10)

Other 84,121 (.17)

Floor Material n(%)

Cement brick 24,002 (.09)

Cement Cake 136,302 (.51)

Dirt 87,078 (.33)

Other 3,185 (.07)

Roof Material n(%)

Metal sheet 219,549 (.83)

Concrete 22,824 (.09)

Palm leaf 13,498 (.05)

Other 9,124 (.03)

Table 6 The most common exterior house materials, floor materials, and roof material within the Guatemalan households surveyed.

Connected to Service n(%yes)

Water 168,474 (.64)

Sewer 87,584 (.33)

Electricity 217,513 (.82)

Telephone 10,527 (.04)

Trash (public or private) 61,465 (.23)

Table 7 The amount of Guatemalan households surveyed that were connected to water, sewer, electricity,

telephone, and trash services.

Belongings n(%yes)

Television 174,073 (.66)

Radio 125,435 (.47)

Recorder 71,037 (.27)

Gas Stove 111,351 (.42)

Refrigerator 75,924 (.29)

Electric Iron 114,495 (.43)

Washing Machine 14,147 (.05)

Bicycle 86,631 (.33)

Car 26,173 (.10)

Table 8 The number of surveyed households with common possessions.

The Food Security Status (FSS) of each Household was determined based on the

number of affirmative answers to the food-related questions on the survey. The mean

food security status of the entire data was 8, a moderately to severe food security status,

depending on if the household has children or not.

Food Security Status n(%)

0 48,078 (.18)

1 48,852 (.18)

2 44,277 (.17)

3 124,022 (.47)

Table 9 The food security status of the households In Guatemala responding to the survey. The classifications were determined by ELCSA.

Criterion Validity

Next, a bivariate analysis was conducted to compare the food security status to

each of the reported conditions of the households. Statistically significant differences

(p=0.000) were found in all of the 1-way ANOVA test run between the principal factors

and the food security status generated in this study. The principal factor (mean value) of

household materials, access to public services, number of appliances and vehicles, and

level of crowding were all found to be significantly different from the food security status

classified by ELCSA.

Bonferroni tests run within each principal component found a statistically

significant difference (p=0.000) between each individual factor within the component.

Food Security Status Urban, n(%) Rural, n(%)

0 19,083 (38) 29,040 (15)

1 17,605 (23) 31,246 (16)

2 10,826 (14) 33,450 (17)

3 27,786 (36) 96,235 (50)

Table 10 Food Security Status as compared to the location of the household. There is a significant difference between the food security status and the location of the household (p=0.000).

Figure 1 The food security status determined by this study compared to the exterior material of the household. There was a significant difference found between the food security status and the exterior material of the household principal component (p=0.000).

Figure 2 Food Security Status compared to the material of the floor. A significant difference was found between the FSS and the principal component, “floor material.”

Food Security Status Versus House Exterior Materials

0

10

20

30

40

50

60

70

80

0 1 2 3

Food Security Status

Perc

en

t

BlockWoodAdobe

Food Security Status Versus Floor Material

0

10

20

30

40

50

60

0 1 2 3

Food Security Status

Perc

en

t (%

)

TilecementDirt

Figure 3 The FSS compared to the roof material of each household surveyed. A significant difference was determined between the FSS and principal component, ‘roof material” (P=0.000).

Figure 4 The comparison of FSS to the possessions in the household. There was a significant difference found between the FSS and principal component, “belongings and vehicles (Figure 5 )”( p=0.000).

Belongings Versus Food Security Status

0

10

20

30

40

50

60

70

80

90

1 2 3 4

Food Security Status

Perc

en

t (%

) Y

es Television

Radio

Recorder

Iron

Roof Material Versus Food Security Status

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3

Food Security Status

Perc

en

t (%

)

Palm LeafMetalConcrete

Figure 5 The FSS compared to Appliances and Vehicles. A significant difference was determined between

the FSS and the possession of Appliances and Vehicles (p=0.000).

Figure 6 The Food security status compared to the poverty level of the household, as classified by the Guatemala National Institute of Statistics.

Poverty Level Versus Food Security Status

0

10

20

30

40

50

60

70

0 1 2 3

Food Security Status

Perc

en

t (%

)

not poorpoorvery poor

Appliances/Vehicles Versus Food Security Status

0

10

20

30

40

50

60

70

0 1 2 3

Food Security Status

Perc

en

t (%

) Y

es

StoveRefrigeratorWashing MachineBikeCar

Figure 7 Food security status compared to access to services (water, electricity, and telephone). A significant difference was found between the FSS generated and the principal component “Access to services” (p=0.000).

Acess to Services Versus Food Security Status

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3

Food Secuirty Status

Perc

en

t (%

) Y

es

WaterElectricityTelephone

Internal Validity

Question Number

DIF

Measure

DIF S.E. Person/Class DIF

Measure

DIS S.E DIF Contrast

1 -5.16 .03 2 -4.51 .02 -.66

2 -1.56 .02 2 -1.56 .01 .00

3 -0.15 .02 2 -.40 .01 .25

4 -1.15 .02 2 -1.01 .01 -.15

5 .77 .02 2 .80 .01 -.02

6 -.36 .02 2 -.18 .01 -.18

7 1.02 .02 2 1.18 .01 -.16

8 1.92 .02 2 2.12 .01 -.20

9 -.76 .02 2 -.96 .01 .20

10 -.54 .02 2 -.78 .01 .23

11 -.06 .02 2 -.28 .01 .22

12 -.10 .02 2 -.14 .01 .04

13 .89 .02 2 .74 .01 .14

14 2.18 .02 2 2.15 .01 .03

15 2.96 .03 2 2.96 .01 .00

Table 11 The DIF CONTRAST values run between the urban and rural settings of the households who

responded to the survey.

Figure 8 The infit values of each item of the ELCSA survey. What is considered the “good” range, 0.8-1.2, is highlighted.

Within the Rasch analysis, all but one item of the food security scale fell into

what is considered a good range for reliability: 0.8-1.2, (adequate .6-1.4) as highlighted

in Figure 18. The only item falling outside of this range was question one (infit

value=1.39), a value that still is just outside of the 0.7-1.3 adequate range. These infit

values support the internal validity of this tool, suggesting each item is an individual

factor and that the respondent understood the survey and was able to correctly portray

their household experience.

Figure 9 The Severity Values of each item in the Latin American and Caribbean Food Security Scale. Items arranged in order of increasing severity.

Severity Values

-6

-5

-4

-3

-2

-1

0

1

2

3

4

Wor

ried

Adult,

no nut

ritious

/var

ied diet

Adult,

few fo

ods

Child

, no nu

tritio

us/v

aried diet

Child

, few

food

s

HH ran ou

t of f

ood

Adult a

te le

ss

Child

ate le

ss

Child

, ser

ved less

Adult s

kipp

ed

Child

felt

hung

ry

Adult f

elt h

ungr

y

Adult,

one da

y with

out f

ood

Child

wen

t to be

d hu

ngry

Child

, one

day

with

out f

ood

Item

Severi

ty V

alu

e

Discussion

The purpose of this study was to assess the internal and criterion validity of the

Latin America and Caribbean Food Security scale. With this data set, this scale will be

part of a national living conditions survey.

With use of the Rasch model, all but one of the items fell within the

recommended range of infit values (a value of 1 indicates perfect compliance with the

framework), supporting the internal validity of the study. This indicated that the actual

responses were very similar to the expected responses and interviewees generally

understood the survey. The severity of the questions increased in a straight line,

demonstrating what was hypothesized about the different levels of food insecurity, and

confirms the correct order of questions increasing in severity within the questionnaire.

Question two is more severe than question one, representing a physical lack of

food rather than a psychometric measure of food insecurity. Question two also indicates a

more severe level of food insecurity than question three because it measures the quantity

of the food, while question three measures the quality of the food. This confirms that the

quality of the food first diminishes (and is less severe) before the quantity of the food is

affected. The scale is designed this way so that it can be used in the future as a cut off

point to identify food insecure households without having to interpret each item

individually. Typically, when the quality or variety of the food begins to decrease, a

household will move from a food secure status to a mildly food insecure household

status.

Question five represents a higher level of food because it communicates that the

adult not only skipped a meal, but it general reduced their portion size due to a lack of

food. When households begin to decrease the serving size of their meal, or skip a meal

entirely, their status increases to moderately food insecure. The severity then escalates

toward the most severe question on the household questions: did the adult go a full day

without food.

Question nine, asking about the quality of the diet of the child is less severe than

decreasing quantity of the adult’s intake. The severity values in the child portion of the

survey continue to represent greater levels of insecurity ending with the most food

insecure situation: child going the entire day without food. This is the most food insecure

situation possible measured by the scale, for it is assumed that every measure has been

taken to protect the child from the household food insecurity status. An affirmative

answer to questions regarding feeling hunger or going a whole day without food

classifies a household as severely food insecure. This pattern is consistent with the theory

that first quality of adult food decreases, then quantity, followed by quality of child’s

food, and finally quantity of child’s food.

Aside from one value on the DIF CONTRAST test, the differential item

functioning results did not show a significant difference, (p <0.5) indicating there is no

significant difference when applying this survey to an urban versus a rural setting. This

function, along with the Rasch model results, further supports the internal validity of this

study.

The statistically significant results of the 1-way ANOVA display the external

validity of this tool. The correlation between the food security status and the other factors

previously linked to food insecurity implies that this survey can be used to measure

household food insecurity. With these results, one can see that a house with poorer house

construction materials, less access to public services, fewer appliances or vehicles, a

more severe poverty level, and a more crowded house strongly correlates to a more food

insecure household. The trends were the same for every variable studied: as conditions

within the household worsen, the food insecurity status increases. Food insecurity was

also found to be more severe in rural settings and households with children.

It is important to recognize the general pattern of these indicators and understand

the correlation the food security status has with each factor. However, these results do not

mean the indicators are absolute- they do not depend on one another. For example, just

because a house is classified as very poor, does not mean it is necessarily food insecure.

The household could have other means of obtaining food without a strong income. It is

true for the reverse situation as well; just because a house is labeled non-poor, does not

mean it too is automatically food secure. These households may have a steady income,

but may chose to spend their money on other things besides food.

By verifying the validity of the Latin American and Caribbean food Security

scale in Guatemala, the high-risk and at-risk food insecure populations can be targeted

and assisted. Once these households or areas have been identified, the negative effects of

food insecurity, such as vitamin and mineral deficiency or child growth stunting, can be

measured using anthropometric measurements.

Interventions such as fortifying foods, performing nutrition education, or

implementing self-sustaining agriculture programs can help alleviate the problem within

the population. It is then necessary to monitor the intervention and reassess the problem,

such as retaking anthropometric measurements, and determining the efficacy of the

intervention. Governmental Agency, United States Agency of International Development

(USAID), has demonstrated the importance of targeting at-risk populations and the

immense amount of benefits that can result from interventions. USAID contributes

around $16 to $18 million dollars a year to it’s food assistance program in Guatemala,

which improves the food security of nearly half a million poor Guatemalan families4. The

money is split up to help ensure food security around the nation, with around one third of

it being “monetized” for use within the markets and basic health care4. The money is also

used to buy food commodities to distribute to families through community development

programs.

With support from the criterion validity tests, these households can be targeted

not only in Guatemala, but in other countries as well. For example, in 2003, a similar

survey helped target food insecure households, and the Brazilian government

implemented the national program, “Fome Zero (Zero Hunger),” to assist with food

insecurity on the federal, economical, and agricultural level24. The government essentially

created a cycle that paid farmers for their foods produced, and in turn placed those local

foods in public centers for consumption. This program increased income and food

accessibility, while decreasing hunger and food insecurity in previously identified

populations.24 Programs similar to this one can help alleviate hunger and food insecurity

around the world. It is critical first, however, to measure and identify the vulnerable

populations in order to implement successful programs.

Limitations of this study include: non-standardized methodologies of the

interviewers and lack of data from every county in the country of Guatemala. This survey

is one indication of food insecurity; it is not the only or complete measure of food

insecurity within a country.

Conclusion

This study supported the validity of the Latin American and Caribbean Food

Security Scale to measure food insecurity in Guatemala. This tool is cost efficient, simple

to apply and evaluate, and can provide accurate indicators of household food insecurity.

This tool can assist the World Food Summit and governmental agencies around the world

to alleviate the detrimental phenomenon of food insecurity.

Based on these findings, the authors support the use of this tool in nationally

representative surveys to portray the food insecurity phenomenon and to help policy

makers target and more efficiently assist at-risk or high-risk populations in meeting their

nutritional requirements for healthy and productive lives.

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Appendix

The Latin American And Caribbean Food Security Scale

1. During the last three months, were you worried that your household would run out of food because of lack of money or other resources to obtain food? 2. During the last three months, did your household run out of food because of lack of money or other resources to obtain food? 3. During the last three months, did your household lack of enough money or other resources to obtain a nutritious and varied diet? 4. During the last three months, did you or any adult in your household have to consume just one or two kinds of food because of lack of money or other resources to obtain food? 5. During the last three months, did you or any adult in your household not eat breakfast, lunch or dinner because of lack of money or other resources to obtain food? 6. During the last three months, did you or any adult in your household eat less than you thought you should because of lack of money or other resources to obtain food? 7. During the last three months, did you or any adult in your household feel hungry but couldn’t eat because there was neither food nor any way to obtain it? 8. During the last three months, did you or any adult in your household go without eating for a whole day there was neither food nor any way to obtain it? 9. During the last three months, did any child in your household not receive a nutritious and varied diet because of lack of money or other resources to obtain food? 10. During the last three months, did any child in your household have to consume just a few types of food because of lack of money or other resources to obtain food? 11. During the last three months, any child in your household eat less than you thought they should because of lack of money or other resources to obtain food? 12. During the last three months, did you have to serve less food to any child in your household because of lack of money or other resources to obtain food? 13. During the last three months, any child in your household feel hungry but you could not get more food because of lack of money or other resources to obtain food? 14. During the last three months, any child in your household go to bed hungry because of lack of money or other resources to obtain food? 15. During the last three months, any child in your household go without eating for a whole day there was no food nor you had the possibility of obtain it?